Density-Dependent Demographic Variation Determines Extinction Rate of Experimental Populations

Understanding population extinctions is a chief goal of ecological theory. While stochastic theories of population growth are commonly used to forecast extinction, models used for prediction have not been adequately tested with experimental data. In a previously published experiment, variation in available food was experimentally manipulated in 281 laboratory populations of Daphnia magna to test hypothesized effects of environmental variation on population persistence. Here, half of those data were used to select and fit a stochastic model of population growth to predict extinctions of populations in the other half. When density-dependent demographic stochasticity was detected and incorporated in simple stochastic models, rates of population extinction were accurately predicted or only slightly biased. However, when density-dependent demographic stochasticity was not accounted for, as is usual when forecasting extinction of threatened and endangered species, predicted extinction rates were severely biased. Thus, an experimental demonstration shows that reliable estimates of extinction risk may be obtained for populations in variable environments if high-quality data are available for model selection and if density-dependent demographic stochasticity is accounted for. These results suggest that further consideration of density-dependent demographic stochasticity is required if predicted extinction rates are to be relied upon for conservation planning

Relative importance of environmental, geographic, and spatial variables on zooplankton metacommunities

© 2014 Dallas and Drake. Understanding the factors responsible for structuring ecological communities is a central goal in community ecology. Previous work has focused on determining the relative roles of two classes of variables (e.g., spatial and environmental) on community composition. However, this approach may ignore the disproportionate impact of variables within classes, and is often confounded by spatial autocorrelation leading to collinearity among variables of different classes. Here, we combine pattern-based metacommunity and machine learning analyses to characterize metacommunity structure of zooplankton from lakes in the northeast United States and to identify environmental, spatial, and geographic covariates associated with metacommunity structure. Analyses were performed for the entire metacommunity and for three zooplankton subsets (cladocerans, copepods, and rotifers), as the variables associated with community structure in these groups were hypothesized to differ. Species distributions of all subsets adhered to an environmental, spatial, and/or geographic gradient, but differed in metacommunity pattern, as copepod species distributions responded independently of one another, while the entire zooplankton metacommunity, cladocerans, and rotifers replaced one another in discrete groups. While environmental variables were nearly always the most important to metacommunity structure, the relative importance of variables differed among zooplankton subsets, suggesting that zooplankton subsets differ in their environmental tolerances and dispersal-limitation

A Bayesian approach for energy-based estimation of acoustic aberrations in high intensity focused ultrasound treatment

High intensity focused ultrasound is a non-invasive method for treatment of diseased tissue that uses a beam of ultrasound to generate heat within a small volume. A common challenge in application of this technique is that heterogeneity of the biological medium can defocus the ultrasound beam. Here we reduce the problem of refocusing the beam to the inverse problem of estimating the acoustic aberration due to the biological tissue from acoustic radiative force imaging data. We solve this inverse problem using a Bayesian framework with a hierarchical prior and solve the inverse problem using a Metropolis-within-Gibbs algorithm. The framework is tested using both synthetic and experimental datasets. We demonstrate that our approach has the ability to estimate the aberrations using small datasets, as little as 32 sonication tests, which can lead to significant speedup in the treatment process. Furthermore, our approach is compatible with a wide range of sonication tests and can be applied to other energy-based measurement techniques

Extinction hazards in experimental Daphnia magna populations: effects of genotype diversity and environmental variation

Extinction is ubiquitous in natural systems and the ultimate fate of all biological populations. However, the factors that contribute to population extinction are still poorly understood, particularly genetic diversity and composition. A laboratory experiment was conducted to examine the influences of environmental variation and genotype diversity on persistence in experimental Daphnia magna populations. Populations were initiated in two blocks with one, two, three, or six randomly selected and equally represented genotypes, fed and checked for extinction daily, and censused twice weekly over a period of 170 days. Our results show no evidence for an effect of the number of genotypes in a population on extinction hazard. Environmental variation had a strong effect on hazards in both experimental blocks, but the direction of the effect differed between blocks. In the first block, variable environments hastened extinction, while in the second block, hazards were reduced under variable food input. This occurred despite greater fluctuations in population size in variable environments in the second block of our experiment. Our results conflict with previous studies, where environmental variation consistently increased extinction risk. They are also at odds with previous studies in other systems that documented significant effects of genetic diversity on population persistence. We speculate that the lack of sexual reproduction, or the phenotypic similarity among our experimental lines, might underlie the lack of a significant effect of genotype diversity in our study

Environmental Variation, Stochastic Extinction, and Competitive Coexistence

Understanding how environmental fluctuations affect population persistence is essential for predicting the ecological impacts of expected future increases in climate variability. However, two bodies of theory make opposite predictions about the effect of environmental variation on persistence. Single‐species theory, common in conservation biology and population viability analyses, suggests that environmental variation increases the risk of stochastic extinction. By contrast, coexistence theory has shown that environmental variation can buffer inferior competitors against competitive exclusion through a storage effect. We reconcile these two perspectives by showing that in the presence of demographic stochasticity, environmental variation can increase the chance of extinction while simultaneously stabilizing coexistence. Our stochastic simulations of a two‐species storage effect model reveal a unimodal relationship between environmental variation and coexistence time, implying maximum coexistence at intermediate levels of environmental variation. The unimodal pattern reflects the fact that the stabilizing influence of the storage effect accumulates rapidly at low levels of environmental variation, whereas the risk of extinction due to the combined effects of environmental variation and demographic stochasticity increases most rapidly at higher levels of variation. Future increases in environmental variation could either increase or decrease an inferior competitor’s expected persistence time, depending on the distance between the present level of environmental variation and the optimal level anticipated by this theory

Experimental Demonstration of Accelerated Extinction in Source-Sink Metapopulations

Population extinction is a fundamental ecological process which may be aggravated by the exchange of organisms between productive (source) and unproductive (sink) habitat patches. The extent to which such source-sink exchange affects extinction rates is unknown. We conducted an experiment in which metapopulation effects could be distinguished from source-sink effects in laboratory populations of Daphnia magna. Time-to-extinction in this experiment was maximized at intermediate levels of habitat fragmentation, which is consistent with a minority of theoretical models. These results provided a baseline for comparison with experimental treatments designed to detect effects of concentrating resources in source patches. These treatments showed that source-sink configurations increased population variability (the coefficient of variation in abundance) and extinction hazard compared with homogeneous environments. These results suggest that where environments are spatially heterogeneous, accurate assessments of extinction risk will require understanding the exchange of organisms among population sources and sinks. Such heterogeneity may be the norm rather than the exception because of both the intrinsic heterogeneity naturally exhibited by ecosystems and increasing habitat fragmentation by human activity

Quality control of EUVE databases

The publicly accessible databases for the Extreme Ultraviolet Explorer include: the EUVE Archive mailserver; the CEA ftp site; the EUVE Guest Observer Mailserver; and the Astronomical Data System node. The EUVE Performance Assurance team is responsible for verifying that these public EUVE databases are working properly, and that the public availability of EUVE data contained therein does not infringe any data rights which may have been assigned. In this poster, we describe the Quality Assurance (QA) procedures we have developed from the approach of QA as a service organization, thus reflecting the overall EUVE philosophy of Quality Assurance integrated into normal operating procedures, rather than imposed as an external, post facto, control mechanism